Anticipatory depth of field adjustment for optical coherence tomography

ABSTRACT

A system and method for surface inspection of an object using optical coherence tomography (OCT) with anticipatory depth of field adjustment is provided. The method includes determining a present working distance and one or more forward working distances; determining a present depth of field in which the surface of the object is in focus at the location of the present working distance and at as many of the consecutive forward surface locations as determined possible; changing to the present depth of field; performing an A-scan of the object; moving the object such that the scanner head is directed at each of the consecutive forward surface locations determined to be in the present depth of field; and performing an A-scan at each of the consecutive forward surface locations determined to be in the present depth of field.

TECHNICAL FIELD

The following relates generally to imaging and more specifically to asystem and method for anticipatory depth of field adjustment for opticalcoherence tomography.

BACKGROUND

In many applications, imaging can be used to garner information about aparticular object; particularly aspects about its surface or subsurface.One such imaging technique is tomography. A device practicing tomographyimages an object by sections or sectioning, through the use of apenetrating wave. Conventionally, tomography can be used for variousapplications; for example, radiology, biology, materials science,manufacturing, quality assurance, quality control, or the like. Sometypes of tomography include, for example, optical coherence tomography,x-ray tomography, positron emission tomography, optical projectiontomography, or the like.

Conventionally, the above types of tomography, and especially opticalcoherence tomography, produce detailed imaging of an object; however,inaccuracies and problems can arise with respect to properly imaging theobject.

SUMMARY

In an aspect, there is provided a method of surface inspection of amoveable object using optical coherence tomography (OCT), the methodcomprising: determining a first working distance between a scanner headand a first surface location on the object; determining one or moreforward working distances located along the object, opposite a directionof travel of the object, from the first surface location, each forwardworking distance is a distance between the scanner head and a respectiveforward surface location on the object; determining a present depth offield, based on the first working distance and the one or more forwardworking distances, where the surface of the object is within the presentdepth of field at the present surface location and at as many of theconsecutive forward surface locations as determined possible; changing acurrent depth of field to the present depth of field; performing anA-scan of the object at the present surface location; moving the objectalong the direction of travel such that the scanner head is directed ateach of the consecutive forward surface locations determined to be inthe present depth of field; and performing an A-scan at each of theconsecutive forward surface locations determined to be in the presentdepth of field.

In a particular case, after moving the object to a last of theconsecutive forward surface locations determined to be in the presentdepth of field, the method further comprising: determining one or moresubsequent forward working distances located at subsequent forwardsurface locations along the object, opposite the direction of travel ofthe object, from the present surface location; determining the presentdepth of field, based on the forward working distance at the presentsurface location and the one or more subsequent forward workingdistances, where the surface of the object is within the present depthof field at the present surface location and at as many of theconsecutive subsequent forward surface locations as determined possible;changing the current depth of field to the present depth of field;performing an A-scan of the object at the present surface location;moving the object along the direction of travel such that the scannerhead is directed at each of the consecutive subsequent forward surfacelocations determined to be in the present depth of field; and performingan A-scan at each of the consecutive subsequent forward surfacelocations determined to be in the present depth of field.

In another case, at least a portion of the object has a curved surfaceprofile.

In yet another case, determining the first working distance, determiningthe one or more forward working distances, and determining thesubsequent forward working distances each comprise measuring a distancebetween the scanner head and the surface of the object.

In yet another case, the method further comprising retrieving a surfacegeometry of the object from a database, and determining the firstworking distance, determining the one or more forward working distances,and determining the subsequent forward working distances each comprisedetermining a distance between the scanner head and the surface of theobject from the surface geometry of the object.

In yet another case, at least some of the A-scans are aggregatedtogether into a B-scan.

In yet another case, moving the object along the direction of travelcomprises continuously moving the object.

In yet another case, the one or more forward working distances and theone or more subsequent forward working distances are predetermined.

In yet another case, using the A-scans data, the method furthercomprising detecting a feature on a surface or subsurface of the objectusing a neural network trained using a training set, the training setcomprising A-scans data with a known feature.

In yet another case, the neural network comprises a long short-termmemory (LSTM) machine learning approach and a convolutional neuralnetwork machine learning approach.

In yet another case, the method further comprising detecting a locationof the detected feature using the neural network.

In another aspect, there is provided a system for surface inspection ofa moveable object using an optical coherence tomography (OCT) system,the OCT system comprising an optical source to produce an optical beam,a beam splitter to direct derivatives of the optical beam to areflective element and the object and direct optical beams returned fromthe reflective element and the object to a detector for detection of aninterference effect, the system for surface inspection comprising: adistance determination module to determine a first working distancebetween a scanner head of the OCT system and a first surface location onthe object, and to determine one or more forward working distanceslocated along the object, opposite a direction of travel of the object,from the first surface location, each forward working distance is adistance between the scanner head and a respective forward surfacelocation on the object; a depth-of-field module to determine a presentdepth of field, based on the first working distance and the one or moreforward working distances, where the surface of the object is within thepresent depth of field at the present surface location and at as many ofthe consecutive forward surface locations as determined possible; adepth-of-field adjusting mechanism to change a current depth of field tothe present depth of field, the OCT system performing and outputting anA-scan of the object at the present surface location; and an objecttranslator to move the object along the direction of travel such thatthe scanner head is directed at each of the consecutive forward surfacelocations determined to be in the present depth of field, the OCT systemperforming an A-scan at each of the consecutive forward surfacelocations determined to be in the present depth of field.

In a particular case, after the object translator moves the object to alast of the consecutive forward surface locations determined to be inthe present depth of field: the distance determination module determinesone or more subsequent forward working distances located at subsequentforward surface locations along the object, opposite the direction oftravel of the object, from the present surface location; thedepth-of-field module determines the present depth of field, based onthe forward working distance at the present surface location and the oneor more subsequent forward working distances, where the surface of theobject is within the present depth of field at the present surfacelocation and at as many of the consecutive subsequent forward surfacelocations as determined possible; the depth-of-field adjusting mechanismchanges the current depth of field to the present depth of field, theOCT system performing and outputting an A-scan of the object at thepresent surface location; the object translator moves the object alongthe direction of travel such that the scanner head is directed at eachof the consecutive subsequent forward surface locations determined to bein the present depth of field, the OCT system performing an A-scan ateach of the consecutive subsequent forward surface locations determinedto be in the present depth of field.

In another case, at least a portion of the object has a curved surfaceprofile.

In yet another case, the distance determination module determines thefirst working distance, the one or more forward working distances, andthe subsequent forward working distances by measuring a distance betweenthe scanner head and the surface of the object.

In yet another case, the distance determination module retrieves asurface geometry of the object from a database and the distancedetermination module determines the first working distance, the one ormore forward working distances, and the subsequent forward workingdistances by determining a distance between the scanner head and thesurface of the object from the surface geometry of the object.

In yet another case, the object translator moves the object along thedirection of travel by continuously moving the object.

In yet another case, the object translator stops moving the object priorto the performing of the A-scan at the present surface location, at eachof the forward surface locations, and at each of the subsequent forwardsurface locations.

In yet another case, the direction of travel of the object can be alonga two-dimensional plane.

These and other aspects are contemplated and described herein. It willbe appreciated that the foregoing summary sets out representativeaspects of systems and methods to assist skilled readers inunderstanding the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the invention will become more apparent in the followingdetailed description in which reference is made to the appended drawingswherein:

FIG. 1 is schematic diagram of an optical coherence tomography (OCT)system, according to an embodiment;

FIG. 2 is a schematic diagram for a computing module, according to thesystem of FIG. 1;

FIG. 3 is a flowchart for a method for surface inspection of an objectusing optical coherence tomography (OCT), according to an embodiment;

FIG. 4A is a diagrammatic side view of a scanner head and object,according to the system of FIG. 1;

FIG. 4B is a diagrammatic side view of the scanner head and object ofFIG. 4A at a later point in time;

FIG. 5 is a diagrammatic side view of a scanner head and object overtime, according to the system of FIG. 1;

FIG. 6 is a diagrammatic side view of a scanner head and object,according to the system of FIG. 1;

FIG. 7 is a method for determining depth of field, according to anotherembodiment;

FIG. 8 is an illustration of depth of field;

FIG. 9A is an exemplary B-scan in which a defect was detected in a paintlayer of a vehicle part;

FIG. 9B is a plot of a score produced for the exemplary B-scan of FIG.9A;

FIG. 10A is an exemplary B-scan in which the system determined there areno features present;

FIG. 10B is an exemplary B-scan in which the system determined there arefeatures present;

FIG. 11 is an exemplary image captured to form a top-level surface viewof an object;

FIG. 12A is an exemplary B-scan of an object without problematic defectsor features;

FIG. 12B is an exemplary A-scan of the object;

FIG. 13 is an exemplary B-scan of an object with problematic defects orfeatures;

FIG. 14 is an exemplary B-scan of an object for determining whetherthere are defects;

FIG. 15 is an exemplary B-scan of an object showing a defect; and

FIGS. 16A and 16B illustrate, at respectively different angles ofperspective, an exemplary C-scan.

DETAILED DESCRIPTION

Embodiments will now be described with reference to the figures. Forsimplicity and clarity of illustration, where considered appropriate,reference numerals may be repeated among the Figures to indicatecorresponding or analogous elements. In addition, numerous specificdetails are set forth in order to provide a thorough understanding ofthe embodiments described herein. However, it will be understood bythose of ordinary skill in the art that the embodiments described hereinmay be practiced without these specific details. In other instances,well-known methods, procedures and components have not been described indetail so as not to obscure the embodiments described herein. Also, thedescription is not to be considered as limiting the scope of theembodiments described herein.

Various terms used throughout the present description may be read andunderstood as follows, unless the context indicates otherwise: “or” asused throughout is inclusive, as though written “and/or”; singulararticles and pronouns as used throughout include their plural forms, andvice versa; similarly, gendered pronouns include their counterpartpronouns so that pronouns should not be understood as limiting anythingdescribed herein to use, implementation, performance, etc. by a singlegender; “exemplary” should be understood as “illustrative” or“exemplifying” and not necessarily as “preferred” over otherembodiments. Further definitions for terms may be set out herein; thesemay apply to prior and subsequent instances of those terms, as will beunderstood from a reading of the present description.

Any module, unit, component, server, computer, terminal, engine ordevice exemplified herein that executes instructions may include orotherwise have access to computer readable media such as storage media,computer storage media, or data storage devices (removable and/ornon-removable) such as, for example, magnetic disks, optical disks, ortape. Computer storage media may include volatile and non-volatile,removable and non-removable media implemented in any method ortechnology for storage of information, such as computer readableinstructions, data structures, program modules, or other data. Examplesof computer storage media include RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed by anapplication, module, or both. Any such computer storage media may bepart of the device or accessible or connectable thereto. Further, unlessthe context clearly indicates otherwise, any processor or controller setout herein may be implemented as a singular processor or as a pluralityof processors. The plurality of processors may be arrayed ordistributed, and any processing function referred to herein may becarried out by one or by a plurality of processors, even though a singleprocessor may be exemplified. Any method, application or module hereindescribed may be implemented using computer readable/executableinstructions that may be stored or otherwise held by such computerreadable media and executed by the one or more processors.

The following relates generally to imaging and more specifically to asystem and method for anticipatory depth of field adjustment for opticalcoherence tomography.

Optical coherence tomography (OCT), and particularly non-destructiveOCT, is a technique for imaging in two or three-dimensions. OCT canprovide a relatively high resolution, potentially up to few micrometers,and can have relatively deep penetration, potentially up to a fewmillimeters, in a scattering media.

OCT techniques can use back-scattered light from an object to generateinformation about that object; for example, generating athree-dimensional representation of that object when different regionsof the object are imaged.

FIG. 1 illustrates a schematic diagram of an OCT system 100, accordingto an embodiment. The OCT system 100 includes an optical source (orphotonic emitter) 102, a reflective element 104 (for example, a mirror),a beam splitter 110, and a detector (for example, a photodetector) 106.The diagram shows an object 108 with three layers of depth. The opticalsource 102 produces an originating optical beam (or path) 112 that isdirected towards the beam splitter 110. The beam splitter 110 dividesthe originating beam 112 and directs one derivative beam (or path) 114towards the reflective element 104 and another derivative beam, referredto herein as the sample beam (or path) 120, towards the object to bescanned 108. Both derivative beams 114, 120 are directed back to thebeam splitter 110, and then directed as a resultant beam 118 to thedetector 106. In some cases, one or more secondary mirrors (not shown)can be provided to reflect the sample beam 120 onto the object 108. Insome cases, there may be a scanner head 121 to direct the sample beam120 onto the object 108. In some cases, the scanner head 121 can includea beam steering device to direct light to the object 108. The beamsteering device may be, for example, a mirror galvanometer in one or twodimensions, a single axis scanner, a microelectromechanical system(MEMs)-based scanning mechanism, a rotating scanner, or other suitablemechanism for beam steering. The beam steering device may be controlledelectromechanically. In some embodiments, as described herein, thescanner head 121 can include a depth-of-field adjusting mechanism 125.

In some cases, the OCT system 100 can include a distance determinationmodule 127 for determining the distance between the scanner head and theobject 108. In an example, the distance determination module 127 can bean infrared line scanner, laser rangefinder, 3D laser scanner, radarbased rangefinder, or the like. In some cases, the distancedetermination module 127 may be associated with, or separate from, thescanner head 121.

In some cases, the system 100 can include an amplification mechanism;for example, a doped fiber amplifier, a semiconductor amplifier, a Ramanamplifier, a parametric amplifier, or the like. The amplificationmechanism can be used to amplify the signal of the optical source 102and/or to increase quantity of photons backscattered off the surfaceunder inspection and collected on the detector 106. By using theamplification mechanism, sensitivity of the system 100 may be increased.

In some cases, the system 100 can include an object translator 109 tomove the object relative to the sample beam 120 and/or the scanner head121. The object translator 109 can be, for example, a conveyor system, arobotic system, or the like. The illustration of FIG. 1 is onlydiagrammatic as the optical paths can be comprised of optical cables,and as such, the system components can have any number of physicalplacements and arrangements.

The optical source 102 can be any light source suitable for use with aninterferometric imaging modality; for example, a laser or light emittingdiode (LED). Particularly, in some implementations, the optical source102 can be a tunable laser the wavelength of which can be altered (i.e.swept) in a controlled manner; for example, to sweep a wide wavelengthrange (e.g. 110 nm) at high speed (e.g. 20 KHz). In a particularexample, the tunable laser can have a centre wavelength of 1310 nm,wherein the wavelength of the emitted light is continuously scanned overa 110 nm range, with a scan rate of 20 kHz and a coherence length ofover 10 mm. In a further embodiment, the optical source 102 may be a lowcoherence light source such as white light or an LED. As an example,using a low coherence light source can facilitate extraction of spectralinformation from the imaging data by distributing different opticalfrequencies onto a detector array (e.g. line array CCD) via a dispersiveelement, such as a prism, grating, or other suitable device. This canoccur in a single exposure as information of the full depth scan can beacquired.

In some cases, the optical source 102 can include a collimator 103 fornarrowing the originating beam 112. In further cases, further optics maybe included in various stages of the system 100 to control or change theoptical beams. Optics may include lenses or other optical devicessuitable to control, guide, navigate, position, or the like, the lightbeam in a desired manner; as an example, an F-theta or telecentric lensmay be included. Where an F-theta or telecentric lens is used, theplanification means that the depth-of-field adjusting mechanism 125 onlyhas to compensate in the axial direction, along the z-axis of theoptical beam, as described below.

In further cases, software techniques may be employed for correcting oraffecting optical errors or signals.

The detector 106 can be any suitable photodetector. In a particularcase, the detector 106 can be a balanced photodetector, which can havean increased signal to noise ratio. In further cases, the detector 106may be a photoelectric-type photodetector, such as a charge-coupleddevice (CCD) or complementary metal-oxide semiconductor (CMOS). Thedetector 106 may operate by photoemission, photovoltaic, thermal,photochemical, or polarization mechanism, or other mechanism throughwhich electromagnetic energy can be converted into an electrical signal.Upon receiving the resultant beam 118, the detector 106 can convert theradiance/intensity of the resultant beam 118 into an electrical signal.In some cases, the electrical signal may then be converted to a digitalsignal, and modified by signal conditioning techniques such as filteringand amplification. In some cases, the interference pattern correspondingto the backscattered light can be converted into a signal by thedetector 106 via, for example, a high-speed digitizer.

The OCT system also includes a computing module 200. The computingmodule 200 may be locally communicatively linked or remotelycommunicatively linked, for example via a network 150, to one or moreother elements of the system 100; for example, to the optical source102, the detector 106, the object translator 109, the scanner head 121,the depth-of-field adjusting mechanism 125, and the distancedetermination module 127. The computing module 200 may be used forprocessing and analysis of imaging data provided by the OCT system 100.In some cases, the computing module 200 may operate as a control systemor controller, and in other cases, may be connected to a separatecontrol system or controller. Further, the computing module 200 may hosta user-accessible platform for invoking services, such as reporting andanalysis services, and for providing computational resources to effectmachine learning techniques on the imaging data.

In an embodiment, as shown in FIG. 2, the computing module 200 caninclude a number of physical and logical components, including a centralprocessing unit (“CPU”) 260, random access memory (“RAM”) 264, an inputinterface 268, an output interface 272, a network interface 276,non-volatile storage 280, and a local bus 284 enabling CPU 260 tocommunicate with the other components. CPU 260 can include one or moreprocessors. RAM 264 provides relatively responsive volatile storage toCPU 260. The input interface 268 enables an administrator to provideinput via, for example, a keyboard and mouse. The output interface 272outputs information to output devices, for example, a display orspeakers. The network interface 276 permits communication with othersystems or computing devices. Non-volatile storage 280 stores theoperating system and programs, including computer-executableinstructions for implementing the OCT system 100 or analyzing data fromthe OCT system 100, as well as any derivative or related data. In somecases, this data can be stored in a database 288. During operation ofthe system 200, the operating system, the programs and the data may beretrieved from the non-volatile storage 280 and placed in RAM 264 tofacilitate execution. In an embodiment, the CPU 260 can be configured toexecute various modules, for example, a depth-of-field module 290 and ageometry module 292.

In some cases, the system 100 can use machine learning (ML) to transformraw data from the A-scan, B-scan, or C-scan into a descriptor. Thedescriptor is information associated with a particular defect in theobject. The descriptor can then be used to determine a classifier forthe defect. As an example, the CPU 260 can do this detection andclassification with auto-encoders as part of a deep belief network.

OCT systems 100 generally use different localization techniques toobtain information in the axial direction, along the axis of theoriginating optical beam 112 (z-axis), and obtain information in thetransverse direction, along a plane perpendicular to the axis of theoriginating beam 112 (x-y axes). Information gained from the axialdirection can be determined by estimating the time delay of the opticalbeam reflected from structures or layers associated with the object 108.OCT systems 100 can indirectly measure the time delay of the opticalbeam using low-coherence interferometry.

Typically, OCT systems that employ low-coherence interferometry can usean optical source 102 that produces an optical beam 112 with a broadoptical bandwidth. The originating optical beam 112 coming out of thesource 102 can be split by the beam splitter 110 into two derivativebeams (or paths). The first derivative beam 114 can be referred to asthe reference beam (or path or arm) and the second derivative beam 120can be referred to as the sample beam (or path or arm) of theinterferometer. Each derivative beam 114, 120 is reflected back andcombined at the detector 106.

The detector 106 can detect an interference effect (fast modulations inintensity) if the time travelled by each derivative beam in thereference arm and sample arm are approximately equal; whereby “equal”generally means a difference of less than a ‘coherence length.’ Thus,the presence of interference serves as a relative measure of distancetravelled by light on the sample arm.

For OCT, the reference arm can be scanned in a controlled manner, andthe reference beam 114 can be recorded at the detector 106. Aninterference pattern can be detected when the mirror 104 is nearlyequidistant to one of the reflecting structures or layers associatedwith the object 108. The detected distance between two locations wherethe interference occurs corresponds to the optical distance between tworeflecting structures or layers of the object in the path of the beam.Advantageously, even though the optical beam can pass through differentstructures or layers in the object, OCT can be used to separate out theamount of reflections from individual structures or layers in the pathof the optical beam.

With respect to obtaining information in the transverse direction, asdescribed below, the sample beam 120 can be focused on a small area ofthe object 108, potentially on the order of a few microns, andsuccessively scanned over a region of the object 108.

In an embodiment of an OCT system, Fourier-domain can be used as apotentially efficient approach for implementation of low-coherenceinterferometry. Instead of recording intensity at different locations ofthe reference reflective element 104, intensity can be detected as afunction of wavelengths or frequencies of the optical beam 112. In thiscase, intensity modulations, as a function of frequency, are referred toas spectral interference. Whereby, a rate of variation of intensity overdifferent frequencies can be indicative of a location of the differentreflecting structures or layers associated with the object. A Fouriertransform of spectral interference information can then be used toprovide information similar to information obtained from scanning of thereflective element 104.

In an embodiment of an OCT system, spectral interference can be obtainedusing either, or both, of spectral-domain techniques and swept-sourcetechniques. With the spectral-domain technique, the optical beam can besplit into different wavelengths and detected by the detector 106 usingspectrometry. In the swept-source technique, the optical beam producedby the optical source 102 can sweep through a range of opticalwavelengths, with a temporal output of the detector 106 being convertedto spectral interference.

Advantageously, employing Fourier-domain can allow for faster imagingbecause back reflections from the object can be measured simultaneously.

The resolution of the axial and transverse information can be consideredindependent. Axial resolution is generally related to the bandwidth, orthe coherence-length, of the originating beam 112. In the case of aGaussian spectrum, the axial resolution (Δz) can be: Δz=0.44*λ₀ ²/Δλ,where λ₀ is the central wavelength of the optical beam and Δλ is thebandwidth defined as full-width-half-maximum of the originating beam. Inother cases, for spectrum of arbitrary shape, the axial spread functioncan be estimated as required.

In some cases, the depth of the topography imaging for an OCT system istypically limited by the depth of penetration of the optical beam intothe object 108, and in some cases, by the finite number of pixels andoptical resolution of the spectrometer associated with the detector 106.Generally, total length or maximum imaging depth z_(max) is determinedby the full spectral bandwidth λ_(ful) of the spectrometer and isexpressed by z_(max)=(1/4N)*(λ₀ ²/λ_(full)) where N is the total numberof pixels of the spectrometer.

With OCT systems, sensitivity is generally dependent on the distance,and thus delay, of reflection. Sensitivity is generally related to depthby: R(z)=sin(p*z)/(p*z)*exp(−z²/(w*p)). Where w depends on the opticalresolution of spectrometer associated with the detector 106. The firstterm related to the finite pixels in the spectrometer and the secondterm related to the finite optical resolution of the spectrometer.

When implementing the OCT system 100, reflected sample and referenceoptical beams that are outside of the coherence length willtheoretically not interfere. This reflectivity profile, called anA-scan, contains information about the spatial dimensions, layers andlocation of structures within the object 108 of varying axial-depths;where the ‘axial’ direction is along the axis of the optical beam path.A cross-sectional tomograph, called a B-scan, may be achieved bylaterally combining a series of adjacent A-scans along an axisorthogonal to the axial direction. A B-scan can be considered a slice ofthe volume being imaged. One can then further combine a series ofadjacent B-scans to form a volume which is called a C-scan. Once animaging volume has been so composed, a tomograph, or slice, can becomputed along any arbitrary plane in the volume.

A-scans represent an intensity profile of the object, and its values (orprofile) characterize reflectance of the way the optical beam penetratesthe surface of the object. Thus, such scans can be used to characterizethe material from the surface of the object to some depth, at anapproximately single region of the object 108. As used in the presentdisclosure, the term ‘surface’, of an object, is understood to includethe peripheral surface down to the depth of penetration of the A-scan.B-scans can be used to provide material characterization from thesurface of the object 108 to some depth, across a contour on the surfaceof the object 108.

The system 100, as described herein, can be used to detect featuresassociated with the surface and subsurface of an object; and in somecases, for later categorization of such features. In a particular case,such features are defects in the object, due to, for example, variousmanufacturing-related errors or conditions.

In some cases, the depth-of-field adjusting mechanism 125 can be, forexample, a focus-tuneable lens. In a particular example, thefocus-tuneable lens can be a liquid lens. With liquid lenses, each lensis filled with an optical liquid. When a user or system applies avoltage, the change in voltage alters the pressure profile of theliquid, resulting in a change in radius of curvature to each lens. Thischange in radius causes the lens to change the effective depth of fieldof the sample beam 120, for example, in the range of +15 to +120 mmusing an aperture of 10 mm. In further cases, the depth-of-fieldadjusting mechanism 125 can be two or more lens that are mechanicallytranslated relative to each other, thereby changing the effective depthof field of the sample beam 120.

In many circumstances, the object to be scanned by the OCT system 100 isnot flat and can have a curved or otherwise modulating surface orprofile. FIGS. 4A and 4B show a portion of an object 408 having a curvedsurface to be scanned; and in some cases, the curved surface may beconsidered monotonic. FIG. 4A illustrates a scanner head 121 scanningthe object 408 at a first instance of time and FIG. 4B shows suchscanner head 121 scanning the object 408 at a later instance of time. Inthis case, the object 408 is moved in the direction illustrated by arrow407 relative to the fixed scanner head 121.The scanner head 121 isdirected at the object 408 and includes the depth-of-field adjustingmechanism 125. A present working distance (“WD”) 402 is illustrated asthe distance between the scanner head 121 or the depth-of-fieldadjusting mechanism 125 and the surface of the object 408. A presentdepth of field 404 (or “confocal parameter” if the beam is assumed to beGaussian) is also illustrated and represents the distance over which thesurface of the object 408 is in focus during the A-scan. Depth of field,as used herein, is understood to be a distance about a plane of focus(POF) in which objects in an image appear sufficiently sharp, and thus,the objects are in focus. In some cases, as exemplified in greaterdetail in FIG. 6, the upper boundary of the depth of field 404, beingthe boundary closest to the scanner head 121, can be called a focalreference plane. The lower boundary of the depth of field 404, being theboundary farthest from the scanner head 121, can be called the workingdistance deviation.

As shown in FIG. 4B, as the object is moved along its direction oftravel, the working distance 402 can increase or decrease due to themodulations in the profile of the object 408; in this case, the workingdistance 402 increased as the surface became further away from thescanner head 121 along the axial direction. As shown in FIG. 4B, theincrease was such that the depth of field 404 no longer includes thesurface of the object 408 within operational focus.

One approach to resolve the above problem is to adjust the path lengthof the reference arm 114. In an example, this can include having thereflective element 104 on a motorized mechanism that translates themirror along the axial direction of the reference beam 114 in order toshorten or lengthen the path length. In another example, this caninclude incorporating a liquid lens in the reference path 114 in orderto change the effective reference path length. However, adjusting thepath length may be inadequate for applications where the object ismoving relative to the scanner head, due to computational and speedconcerns.

In the present embodiments, the system 100 can use the depth-of-fieldadjusting mechanism 125 to change the depth of field of the sample beam120 to adjust for the changes in the profile of the surface of theobject 408. As shown in FIG. 5, the depth-of-field adjusting mechanism125 changes the depth of field over time as the object 408 passes by thescanner head. FIG. 5 illustrates successive positions, over time, of theobject 408 as it moves in direction 407 relative to the fixed scannerhead 121. For reference, a time scale 502 is illustrated withdemarcations 504 to 526 each representing divisions of time. Asillustrated, the depth of field 408 is adjusted by the depth-of-fieldadjusting mechanism 125 in order to ensure that the surface of theobject 408 is within the depth of field.

Referring now to FIG. 3, shown therein is a method 300 of surfaceinspection using the OCT system 100, in accordance with an embodiment.The method 300 may be used for inspecting the surface of an object 408when the object 408 is moved relative to the scanner head 121. In anexemplary case, the method can be for the purposes of detecting surfacedefects or irregularities. In this embodiment, the system 100 candetermine the working distance at some distance ahead of the scannerhead 121, along the axis of movement of the object 408. Thisdetermination can be via either having the distance determination module127 at a predetermined distance ahead of the scanner head 121, by havingone or more additional distance determination modules 127 at apredetermined distance ahead of the scanner head 121, or via determiningthe working distance from the surface geometry at a predetermineddistance ahead of the scanner head 121 by the geometry module 292, or acombination of the above. In some cases, the distance determinationmodule 127 can comprise the geometry module 292 or perform itsfunctions. In this way, the system 100 can anticipate upcoming changesin the surface profile of the object 408 and adjust the depth of fieldaccordingly.

At block 302, the system 100 determines a present working distance,being the distance from the scanner head 121 to the surface of theobject 408, for example, with the distance determination module 127. Atblock 304, the system 100 determines one or more forward workingdistances, for example, with the distance determination module 127. Theone or more forward working distances are each determined at a locationthat is at a distance in front of the present working distance oppositethe direction of travel 407 of the object 408. As an example, in FIG. 5,if the present working distance is determined at the location demarcatedby time division 504, then one of the forward working distances can bedetermined at the location demarcated by time division 506 and anotherone of the forward working distances can be determined at the locationdemarcated by time division 508.

At block 306, the depth-of-field module 290 determines a present depthof field that has the object's 408 surface in focus at the presentworking distance and, to the extent possible, at as many successivelyadjacent forward working distances for which working distances have beendetermined in 304. As an example, in FIG. 5, determining a depth offield that has the surface of the object 408 in focus at the locationdemarcated by time division 504, then at the location demarcated by timedivision 506, then at the location demarcated by time division 508, thenat the location demarcated by time division 510, and so on. As shown inFIG. 5, the depth-of-field module 290 can determine a depth of fieldthat has the object's 408 surface in focus at the locations demarcatedby time divisions 504, 506 and 508; but not at the location demarcatedby time division 510 because the surface would be outside of the depthof field common to the locations demarcated by time divisions 504, 506and 508.

At block 308, the CPU 260 directs the depth-of-field adjusting mechanism125 to adjust the current depth of field to the determined present depthof field 404.

At block 310, the system 100 performs successive A-scans of the object408, as described herein, at the location of the present workingdistance, and then at the location of each of the forward workingdistances determined to be within the present depth of field as theobject 404 is moved by the object translator 109.

At 312, as time progresses and the object 404 is moved by the objecttranslator 109, the system 100 continues determining one or more forwardworking distances ahead of the location presently scanned. At 314, whenthe CPU 260 determines that the scanner head 121 has reached a presentworking distance at a present location having the object's surface notin the depth of field, then at 316, the depth-of-field module 290determines a present depth of field that has the object's 408 surface infocus at the present working distance and, to the extent possible, at asmany successively adjacent forward working distances for which workingdistances have been determined in 312. At block 318, the CPU 260 directsthe depth-of-field adjusting mechanism 125 to adjust the current depthof field to the determined new depth of field.

The system 100 repeats blocks 310 to 318 for successive divisions oftime as the object is moved along by the object translator 109. Thesesuccessive A-scans can be aggregated by the computing module 200. Theaggregation technique may involve stacking images comprising the imagingdata according to image processing techniques. In an embodiment,aggregation of imaging data may include the formation of a B-scan from aplurality of A-scans. In some cases, the B-scan and/or A-scans arepresented to a user via the output interface 272.

The method 300 has the intended advantage of effectively ‘looking ahead’such that the depth of field is adjusted to anticipate upcoming changesin the profile of the object. In this way, the depth of field requiresless adjustment and, in some cases, allows the system 100 to complete aB-scan more quickly, or allows a B-scan to comprise more A-scans whenthe object is moving at a fixed rate.

In method 300, it is generally understood that the surface of the object408 being within the boundaries of the depth of field 404 includesapproximately the totality of the A-scan axial depth being within theboundaries of the depth of field 404.

Advantageously, the approach described herein does not require that thedepth of field be changed for every A-scan or every set of A-scans,which can be slow, especially for applications where the object 408 ismoving relative to the scanner head 121. Rather, the approach describedherein advantageously only requires a change in depth of field whennecessitated by the profile of the object 408.

In some embodiments, the working distance can be determined by analysisof the A-scan to determine if the object 408 is within the depth offield 404. However, this approach is limited in speed and responsivenessdue to having to analyze the A-scan and then having to retake the A-scanif it is not in focus. Advantageously, the method 300 measures theworking distance prior to the A-scan, such that it is faster and moreefficient, and is able to scan an object 408 in movement.

In a particular embodiment of method 300, as shown in FIG. 7, the depthof field 404 can be initially determined, prior to considering theforward working distances, at block 306 and/or block 316, by method 700.At block 702, the working distance is determined by the distancedetermination module, being the distance from the scanner head 121 tothe surface of the object 408. At block 704, the distance determinationmodule sets the determined distance as a “centric working distance”. Atblock 706, the distance determination module subtracts half of the valueof the A-scan depth of field 404 from the centric working distance andsets this result as the “focal reference plane”, as defined above. Atblock 708, the distance determination module sets the “working distancedeviation”, as defined above, below the focal reference plane as thedepth of field 404.

As an example of the embodiments described herein, as shown in FIG. 8,there is an optical setup with 7 mm of depth of field, with an objecthaving an inclined surface at 30° inclination. With this configuration,the depth of field would have to be changed approximately ten times(meaning there are ten focus quantization levels) in order to have theinclined target in focus. While the object is moved in relation to thescanner head, the focus would be shifting over such time.Advantageously, the depth of field 404 can be effectively used as a lowpass filter decreasing the speed and diopters that the focus needs to beadjusted in a single B-scan.

While in the present embodiments the object is described as ‘moving’ viathe object translator 109, it is appreciated that moving can includesuccessively moving and stopping the object 108 for scanning, or caninclude continuously moving the object. Additionally, while in thepresent embodiments, the movement is shown along a single dimensionalaxis, it is appreciated that the movement of the object can be along atwo-dimensional plane.

In some embodiments, the depth-of-field adjusting mechanism 125 may notcapable of adjusting the depth of field quickly enough for each A-scan.In this case, the system 100 can take an A-scan at the appropriate speedof the depth-of-field adjusting mechanism 125 and use digital signalprocessing or machine learning techniques to fill in for the missingintermediate A-scans in the aggregated B-scan.

In some cases, after the A-scans, B-scans, and/or C-scans have beendetermined, the system can detect whether there are defects in theobject using image interpretation and machine learning techniques. Thedefective label indicates that an unacceptable defect has been detected,and in some cases, such defect is of a particular type. In the examplewhere the object is a vehicle part, the defect may have different shapesand dimensions. As an example, the defect may be an unwanted round seedor crater, or the like, on or under the surface of the part. As anotherexample, the defect may have an elongated shape, such as with anunwanted fiber, or the like, on or under the surface of the part. As anexample, the acceptable/defective label may be with regards to the size,area, or volume of a defect. In another example, acceptable/defectivelabel may be with regards to the presence of defect between differentlayers of films applied in an industrial process; for example, in anautomotive setting, in an electro-deposition (ED) layer, a colour layer,or a clear layer, where each layer is in the order of tens of micronsthick.

In some cases, based on analysis of the OCT images, the system 100 canprovide further information in the form of feature localization on theobject. As an example, the information may be that there is fiber defectat location x=3.4 cm, y=5.6 cm on a vehicle part. Feature localizationcan also be specified with respect to surface depth, along the z-axis.Depth localization can be particularly advantageous in certainapplications; for example, when thin films are being applied to avehicle part. In this case, for example, after a vehicle part ispainted, paint inspection may be required on various layers including anelectro-deposition layer, a colour layer, and a clear coat layer. Beingable to detect and determine the presence of a defect between any two ofthese layers is particularly advantageous because it has implications onthe amount of re-work that may be required to resolve the imperfection.It can also be advantageous for improvement to a manufacturing processby being able to determine what type of defect is located at what layer;for example, a faulty HVAC system in the manufacturing environment couldbe responsible for introducing defects between layers. In this regard,being able to localize defect origin to a portion of the manufacturingpath is an advantage to reduce future defects and rework.

The machine-learning techniques described herein may be implemented byproviding input data to a neural network, such as a feed-forward neuralnetwork, for generating at least one output. The neural network may havea plurality of processing nodes, including a multi-variable input layerhaving a plurality of input nodes, at least one hidden layer of nodes,and an output layer having at least one output node. During operation ofa neural network, each of the nodes in the hidden layer applies afunction and a weight to any input arriving at that node (from the inputlayer or from another layer of the hidden layer), and the node mayprovide an output to other nodes (of the hidden layer or to the outputlayer). The neural network may be configured to perform a regressionanalysis providing a continuous output, or a classification analysis toclassify data. The neural networks may be trained using supervised orunsupervised learning techniques. According to a supervised learningtechnique, a training dataset is provided at the input layer inconjunction with a set of known output values at the output layer; forexample, imaging data for which defect location and/or existence isknown. During a training stage, the neural network may process thetraining dataset. It is intended that the neural network learn how toprovide an output for new input data by generalizing the information itlearns in the training stage from the training data. Training may beaffected by back-propagating error to determine weights of the nodes ofthe hidden layers to minimize the error. The training dataset, and theother data described herein, can be stored in the database 288 orotherwise accessible to the computing module 200. Once trained, oroptionally during training, test data can be provided to the neuralnetwork to provide an output. The neural network may thuscross-correlate inputs provided to the input layer in order to provideat least one output at the output layer. Preferably, the output providedby the neural network in each embodiment will be close to a desiredoutput for a given input, such that the neural network satisfactorilyprocesses the input data.

In some embodiments, the machine learning techniques can employ, atleast in part, a long short-term memory (LSTM) machine learningapproach. The LSTM neural network allows for quickly and efficientlyperforming group feature selections and classifications.

In some embodiments, the detection can be by employing, at least inpart, a convolutional neural network (CNN) machine learning approach.

While certain machine-learning approaches are described, specificallyLSTM and CNN, it is appreciated that, in some cases, other suitablemachine learning approaches may be used where appropriate.

As an example, FIG. 9A illustrates an B-scan in which a defect wasdetected in a paint layer of a vehicle part. As shown, the defect iscentered at approximately 225×10⁻² mm along the fast scan axis (x-axis).Correspondingly, FIG. 9B illustrates a plot of a score produced by theCPU 260, between 0 and 1, representing a determined possibility that adefect is present in the exemplary B-scan of FIG. 9A.

As an example, FIG. 10A illustrates a B-scan in which contours areoutlined. In this case, the CPU 260 determined that there was no defectdetected on the object. FIG. 10B also illustrates a B-scan in whichcontours are outlined. In this case, the CPU 260 determined that therewas a defect detected on the object.

FIG. 11 illustrates an exemplary image captured to form a top-levelsurface view of an object.

FIG. 12A illustrates an exemplary B-scan (cross-section) of an objectwithout problematic defects or features (i.e., a ‘clean’ surface).

FIG. 12B illustrates an exemplary A-scan from the center of the B-scan.

FIG. 13 illustrates an exemplary B-scan (cross-section) of an objectwith a problematic defect or feature present. In this case, as shown,there was a subsurface seed detected, centered at approximately 500along the x-axis.

FIG. 14 illustrates an exemplary B-scan of a vehicle part fordetermining whether there are painting defects. In this case, there wasno defect from the B-scan. FIG. 15 illustrates an exemplary B-scan of avehicle part for determining whether there are painting defects. In thiscase, as shown, there was a defect in the paint layer detected, centeredat approximately 225 along the x-axis.

FIGS. 16A and 16B illustrate, at respectively different angles ofperspective, an exemplary C-scan of a vehicle part. In this case, a seedwas detected as a defect in the painting of a vehicle part.

In further embodiments, machine learning can also be used by the CPU 260to detect and compensate for data acquisition errors at the A-scan,B-scan and C-scan levels.

The embodiments described herein include various intended advantages. Asan example, instead of having to change depth of field and/or referencepath length for every A-scan, the embodiments described herein allow fordepth of field adjustment only when required, allowing A-scans to becompleted more quickly or allowing more A-scans to be undertaken.

Although the invention has been described with reference to certainspecific embodiments, various modifications thereof will be apparent tothose skilled in the art without departing from the spirit and scope ofthe invention as outlined in the claims appended hereto. The entiredisclosures of all references recited above are incorporated herein byreference.

1-19. (canceled)
 20. A method of surface inspection of an object usingoptical coherence tomography (OCT), the OCT having an associated tunabledepth of field comprising a distance range, the method comprising:determining a present depth of field having a series of surfacelocations on the object located within the associated distance range;tuning the tunable depth of field to the present depth of field; andperforming a scan of the object at each surface location in the seriesof surface locations.
 21. The method of claim 20, wherein the object ismoveable, and wherein the series of the surface locations aresequentially located opposite a direction of travel of the object. 22.The method of claim 20, wherein the scan of the object comprises anA-scan.
 23. The method of claim 21, wherein the scan for each of thesurface locations that are sequentially located opposite the directionof travel of the object are combined to form a B-scan.
 24. The method ofclaim 20, further comprising: determining a subsequent depth of fieldhaving a subsequent series of surface locations on the object locatedwithin the associated distance range; tuning the tunable depth of fieldto the subsequent depth of field; and performing a scan of the object ateach surface location in the subsequent series of surface locations. 25.The method of claim 24, wherein determining the present depth of fieldand the subsequent depth of field each comprise measuring a distancebetween the scanner head and each surface location of the object. 26.The method of claim 24, wherein determining the present depth of fieldand the subsequent depth of field each comprise determining a distancebetween the scanner head and each surface location of the object usingsurface geometry of the object.
 27. The method of claim 22, using theA-scans data, the method further comprising detecting a feature on asurface or subsurface of the object using a neural network trained usinga training set, the training set comprising A-scans data labelled withthe feature.
 28. The method of claim 27, wherein the feature comprises aworking distance of the surface of the object from a scanner head. 29.The method of claim 28, further comprising further tuning the tunabledepth of field based on the working distance.
 30. A system for surfaceinspection of an object using optical coherence tomography (OCT), theOCT having a tunable depth of field comprising a distance range, thesystem for surface inspection comprising: a depth-of-field module todetermine a present depth of field having a series of surface locationson the object located within a distance range associated with thepresent depth of field; a depth-of-field adjusting mechanism to tune thetunable depth of field to the present depth of field; and a scanner headto perform a scan of the object at each surface location in the seriesof surface locations by directing optical beams towards the object anddetecting optical beams returning from the object.
 31. The system ofclaim 30, further comprising an object translator to move the objectalong a direction of travel, and wherein the series of the surfacelocations are sequentially located opposite the direction of travel. 32.The system of claim 30, wherein the scan of the object comprises anA-scan.
 33. The system of claim 31, wherein the scan for each of thesurface locations that are sequentially located opposite the directionof travel of the object are combined to form a B-scan.
 34. The system ofclaim 30, wherein the depth-of-field module further determines asubsequent depth of field having a subsequent series of surfacelocations on the object located within a distance range associated withthe subsequent depth of field, wherein the depth-of-field adjustingmechanism further tunes the tunable depth of field to the subsequentdepth of field, and wherein the scanner head further performs a scan ofthe object at each surface location in the subsequent series of surfacelocations by directing optical beams towards the object and detectingoptical beams returning from the object.
 35. The system of claim 30,wherein the depth-of-field adjusting mechanism comprises afocus-tuneable lens.
 36. The system of claim 35, wherein thefocus-tuneable lens comprises a liquid lens.
 37. The system of claim 35,wherein the focus-tuneable lens comprises two or more lenses that aremechanically translated relative to each other.
 38. The system of claim32, further comprising a computing module to, using the A-scan data,detect a feature on a surface or subsurface of the object using a neuralnetwork trained using a training set, the training set comprisingA-scans data labelled with the feature.
 39. The system of claim 38,wherein the feature comprises a working distance of the surface of theobject from a scanner head.